Conventional echo cancellers have a linear filter and a nonlinear processor (i.e., NLP) following the filter. A signal received via a network and processed by the linear filter is nonlinearly attenuated with the NLP in certain talking modes (i.e., far end speech). The presence of the NLP increases an echo return loss enhancement (i.e., ERLE), but at the same time decreases interactivity of a conversation (i.e., double-talking performance). Therefore, a common echo canceller seeks an optimal point between the interactivity and the ERLE.
Under steady state conditions, the echo canceller tracks only slow changes of the echo path and removes most of the echoes. However, quick changes in the magnitude of the echo level in the received signal occur from time to time. For example, a quick change occurs if somebody picks up or replaces a parallel handset in a house. Such situations produce considerable changes in an echo path of the network. Typical echo cancellers re-converge relatively slowly in such conditions, so a significant degradation in the system performance exists until the echo canceller re-converges on the new echo path characteristics.
An International Telecommunication Union-Telecommunications Standardization Sector (ITU-T) Recommendation G.168 includes a number of tests for rapid convergence and re-convergence in different modes, and certain echo path changes. ITU-T G.168 test No. 2 concerns convergence and steady state residual and returned echo level tests. A part of the tests imply that once the echo path change occurs, an echo path change (i.e., EPC) procedure and re-converge is run and corrections accomplished within a specific amount of time.
It would be desirable to implement a fast echo gain change detection.